Brokering for the primary healthcare needs of recent immigrant families in Atlantic, Canada
Bibliographic record
Abstract
AIM: This case study describes how broker organizations supported a network of community-based services to work together to address the primary healthcare needs of recent immigrant families with young children. BACKGROUND: In parts of Canada with low levels of immigration compared with large urban centres, service providers may need to collaborate more closely with one another so that cultural competencies and resources are shared. Providers within Atlantic Canada, with its relatively small immigrant population, were faced with such a challenge. METHODS: Social network analysis and qualitative inquiry were the methods used within this case study. Twenty-seven organizations and four proxy organizations representing other organization types were identified as part of the network serving a geographically bounded neighbourhood within a mid-sized urban centre in Atlantic Canada in 2009. Twenty-one of the 27 organizations participated in the network survey and 14 key informants from the service community were interviewed. Findings Broker organizations were identified as pivotal for ensuring connections among network members, for supporting immigrant family access to services through their involvement with multiple providers, and for developing cultural competence capacities in the system overall. Network cohesiveness differed depending on the type of need being addressed, as did the organizations playing the role of broker. Service providers were able to extend their reach through the co-location of services in local centres and schools attended by immigrant families and their children. The study demonstrates the value of ties across service sectors facilitated by broker organizations to ensure the delivery of comprehensive services to young immigrant families challenged by an unfamiliar system of care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.027 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".